PyTorch Image Transformations

• In this notebook, I explored various image transformations using PyTorch's torchvision.transforms.
• The following transformations were applied to an Image of a Beautiful Cat

1. Resize: Resizing the image to a specified size.
2. Gray Scale: Converting the image to grayscale.
3. Normalize: Normalizing pixel values to have zero mean and unit variance.
4. Random Rotation: Applying a random rotation to the image.
5. Center Crop: Center-cropping the image.
6. Random Crop: Applying a random crop to the image

• The Image used is this one with JPEG format,size :(4288, 2848) and RGB model.

  

from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import sys
import torch
import numpy as np
import torchvision.transforms as transforms

• Resize
• Gray Scale
• Normalize
• Random Rotation
• Center Crop
• Random Crop

# Open the image file
img = Image.open("Data/cat.jpg")

# Display some information about the image
print(img.format)
print(img.size)
print(img.mode)

# Show the image 
img.show()

JPEG
(4288, 2848)
RGB

# Define function to show image
def Show_Image(Image, Picture_Name):
    plt.imshow(Image)
    plt.title(Picture_Name)
    plt.show()

# Resize Transformation
Resize_Transformation = transforms.Compose([
   transforms.ToPILImage(), 
   transforms.Resize(size=(850,850)),
])


# View rsult
Resized_Img = Resize_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Resized_Img, 'Resized Image')

# Horizontal Flipping
Horizontal_Flipping_Transformation = transforms.Compose([
    transforms.ToPILImage(),
    transforms.RandomHorizontalFlip(p=1) 
])

# View result
Flipping_Img = Horizontal_Flipping_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Flipping_Img, 'Flipped Image')

# Vertical Flipping
Vertical_Flipping_Transformation = transforms.Compose([
    transforms.ToPILImage(),
    transforms.RandomVerticalFlip(p=1) 
])

# View Result
Flipping_Img = Vertical_Flipping_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Flipping_Img, 'Flipped Image')

# Color Transformation

Color_Transformation = transforms.Compose([
    transforms.ToPILImage(),
    transforms.ColorJitter(brightness=(0.6,0.6), contrast=1,saturation=1, hue=0.4)
])

# View results.
Transformed_Img = Color_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Transformed_Img, 'Transformed Image')

# Random Crop
Crop_Transformation = transforms.Compose([
    transforms.ToPILImage(),
    transforms.RandomCrop(size=(2304,1090))
])

# View output.
Cropped_Img = Crop_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Cropped_Img, 'Cropped Image')

# Rotation
Rotate_Transformation = transforms.Compose([
    transforms.ToPILImage(),
    transforms.RandomRotation(degrees=90)
])

# View results
Rotated_Img = Rotate_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Rotated_Img, 'Rotated Image')

# Normalize Transform 
Normalize_Transformation = transforms.Compose([
    transforms.ToPILImage(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[5,0.9,0.6], std=[1.45,0.21,7.12]), 
    transforms.ToPILImage()
])

# Testing The Transformation...
Normalized_Img = Normalize_Transformation(img)
Show_Image(img, 'Original Image')
Show_Image(Normalized_Img, 'Normalized Image')